Advertisement

Journal of Solution Chemistry

, Volume 47, Issue 5, pp 806–826 | Cite as

The Overlapping Thermodynamic Dissociation Constants of the Antidepressant Vortioxetine Using UV–VIS Multiwavelength pH-Titration Data

  • Milan Meloun
  • Lucie Pilařová
  • Aneta Čápová
  • Tomáš Pekárek
Article

Abstract

Potentiometric and spectrophotometric pH-titrations of the antidepressant drug Vortioxetine were compared for dissociation constants determinations. Vortioxetine is an atypical antidepressant, i.e., it is a serotonin modulator and stimulator. Depressive disorders are common mental health conditions that are thought to be caused by an imbalance in serotonin and norepinephrine in addition to multiple situational, cognitive, and medical factors. A chemometrics approach to the nonlinear regression of the pH-spectra (REACTLAB, SQUAD84) and pH-titration (ESAB) were used to determine the two overlapping dissociation constants. A sparingly soluble neutral base LH of Vortioxetine hydrobromide was protonated to form the two still-soluble cations \( {\text{LH}}_{2}^{ + } \) and \( {\text{LH}}_{ 3}^{{ 2 { + }}} \) in pure water. In the range of pH (5–10), the two dissociation constants could be reliably estimated from small changes in the spectra of 9.2 × 10−5 mol·dm−3 Vortioxetine. Although the change of pH affected changes in the chromophore to a small extent, two thermodynamic dissociation constants were estimated: \( {\text{p}}K_{\text{a1}}^{\text{T}} \) = 7.22 and \( {\text{p}}K_{\text{a2}}^{\text{T}} \) = 8.67 at 25 °C and \( {\text{p}}K_{\text{a1}}^{\text{T}} \) = 7.27 and \( {\text{p}}K_{\text{a2}}^{\text{T}} \) = 8.79 at 37 °C. The graph of molar absorption coefficients of variously protonated species as a function of wavelength shows that the spectra of species \( {\text{LH}}_{2}^{ + } \) and LH vary in color, while protonation of the chromophore \( {\text{LH}}_{2}^{ + } \) to \( {\text{LH}}_{ 3}^{{ 2 { + }}} \) has less influence on the chromophores of the Vortioxetine hydrobromide molecule. Two thermodynamic dissociation constants of 3 × 10−4 mol·dm−3 Vortioxetine were determined by regression analysis of the potentiometric titration curves, \( {\text{p}}K_{\text{a1}}^{\text{T}} \) = 7.08 and \( {\text{p}}K_{\text{a2}}^{\text{T}} \) = 8.50 at 25 °C and \( {\text{p}}K_{\text{a1}}^{\text{T}} \) = 7.33 and \( {\text{p}}K_{\text{a2}}^{\text{T}} \) = 8.76 at 37 °C. A prediction of the dissociation constants of Vortioxetine was carried out using the MARVIN and ACD/Percepta programs and only two dissociation constants were proposed theoretically.

Graphical Abstract

Keywords

Dissociation constants Vortioxetine Spectrophotometric titration REACTLAB SQUAD84 ESAB 

References

  1. 1.
    Lundbeck, H.: FDA accepts Takeda and Lundbeck’s filing for review of Brintellix (vortioxetine) for the treatment of major depression. http://investor.lundbeck.com/releasedetail.cfm?releaseid=726533 (2012)
  2. 2.
    Lundbeck, H.: BRINTELLIX™ (vortioxetine) tablets for oral use. Full Prescribing Information. Pharmacodynamics (2013)Google Scholar
  3. 3.
    Kelliny, M., Croarkin, P.E., Moore, K.M., Bobo, W.V.: Profile of vortioxetine in the treatment of major depressive disorder: an overview of the primary and secondary literature. Ther. Clin. Risk Manag. 11, 1193–1212 (2015)PubMedPubMedCentralGoogle Scholar
  4. 4.
    Kohler, S., Cierpinsky, K., Kronenberg, G., Adli, M.: The serotonergic system in the neurobiology of depression: relevance for novel antidepressants. J. Psychopharm. 30(1), 13–22 (2016)CrossRefGoogle Scholar
  5. 5.
    Rothschild, A.J., Mahableshwarkar, A.R., Jacobsen, P., Yan, M., Sheehan, D.V.: Vortioxetine (Lu AA21004) 5 mg in generalized anxiety disorder: results of an 8-week randomized, double-blind, placebo-controlled clinical trial in the United States. Eur. Neuropsychopharm. 22(12), 858–866 (2012)CrossRefGoogle Scholar
  6. 6.
    Meeker, A.S., Herink, M.C., Haxby, D.G., Hartung, D.M.: The safety and efficacy of vortioxetine for acute treatment of major depressive disorder: a systematic review and meta-analysis. Syst. Rev. 4, 21 (2015)CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Katona, C., Hansen, T., Olsen, C.K.: A randomized, double-blind, placebo-controlled, duloxetine-referenced, fixed-dose study comparing the efficacy and safety of Lu AA21004 in elderly patients with major depressive disorder. Int. Clin. Psychopharm. 27(4), 215–223 (2012)CrossRefGoogle Scholar
  8. 8.
    Pathare, B., Tambe, V., Patil, V.: A review on various analytical methods used in determination of dissociation constant. Int. J. Pharm. Pharmaceut. Sci. 6(8), 26–34 (2014)Google Scholar
  9. 9.
    Reijenga, J., Hoof, A.V., Loon, A.V., Teunissen, B.: Development of methods for the determination of pKa values. Anal. Chem. Insights 8, 53–71 (2013)CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Hernández, J.A., Hernández, A.R., Urbina, E.M.C., Rodríguez, I.M.D.L.G., Manzanares, M.V., Medina-Vallejo, L.F.: New chemometric strategies in the spectrophotometric determination of pKa. Eur. J. Chem. 5(1), 1–5 (2014)CrossRefGoogle Scholar
  11. 11.
    Shayesteh, T.H., Radmehr, M., Khajavi, F., Mahjub, R.: Application of chemometrics in determination of the acid dissociation constants (pK(a)) of several benzodiazepine derivatives as poorly soluble drugs in the presence of ionic surfactants. Eur. J. Pharm. Sci. 69, 44–50 (2015)CrossRefPubMedGoogle Scholar
  12. 12.
    Pandey, M.M., Jaipal, A., Kumar, A., Malik, R., Charde, S.Y.: Determination of pK(a) of felodipine using UV–visible spectroscopy. Spectrochim. Acta A 115, 887–890 (2013)CrossRefGoogle Scholar
  13. 13.
    Manallack, D.T.: The pK(a) distribution of drugs: application to drug discovery. Perspect. Med. Chem. 1, 25–38 (2007)Google Scholar
  14. 14.
    Milletti, F., Storchi, L., Sforna, G., Cruciani, G.: New and original pKa prediction method using grid molecular interaction fields. J. Chem. Inf. Model. 47, 2172–2181 (2007)CrossRefPubMedGoogle Scholar
  15. 15.
    Settimo, L., Bellman, K., Knegtel, R.A.: Comparison of the accuracy of experimental and predicted pKa values of basic and acidic compounds. Pharm. Res. 31, 1082–1095 (2014)CrossRefPubMedGoogle Scholar
  16. 16.
    Tam, K.Y., Takacs-Novak, K.: Multiwavelength spectrophotometric determination of acid dissociation constants: a validation study. Anal. Chim. Acta 434, 157–167 (2001)CrossRefGoogle Scholar
  17. 17.
    Allen, R.I., Box, K.J., Comer, J.E.A., Peake, C., Tam, K.Y.: Multiwavelength spectrophotometric determination of acid dissociation constants of ionizable drugs. J. Pharmaceut. Biomed. 17(4–5), 699–712 (1998)CrossRefGoogle Scholar
  18. 18.
    Hartley, F.R., Burgess, C., Alcock, R.M.: Solution Equilibria. Ellis Horwood, Chichester (1980)Google Scholar
  19. 19.
    Leggett, D.J., McBryde, W.A.E.: General computer program for the computation of stability constants from absorbance data. Anal. Chem. 47(7), 1065–1070 (1975)CrossRefGoogle Scholar
  20. 20.
    Kankare, J.J.: Computation of equilibrium constants for multicomponent systems from spectrophotometric data. Anal. Chem. 42(12), 1322–1326 (1970)CrossRefGoogle Scholar
  21. 21.
    Meloun, M., Ferenčíková, Z., Javůrek, M.: Reliability of dissociation constants and resolution capability of SQUAD(84) and SPECFIT/32 in the regression of multiwavelength spectrophotometric pH-titration data. Spectrochim. Acta A, Mol. Biomol. Spectrosc. 86, 305–314 (2012)CrossRefGoogle Scholar
  22. 22.
    Meloun, M., Nečasová, V., Javůrek, M., Pekárek, T.: The dissociation constants of the cytostatic bosutinib by nonlinear least-squares regression of multiwavelength spectrophotometric and potentiometric pH-titration data. J. Pharmaceut. Biomed. 120, 158–167 (2016)CrossRefGoogle Scholar
  23. 23.
    Meloun, M., Bordovská, S., Syrový, T., Vrána, A.: Tutorial on a chemical model building by least-squares non-linear regression of multiwavelength spectrophotometric pH-titration data. Anal. Chim. Acta 580(1), 107–121 (2006)CrossRefPubMedGoogle Scholar
  24. 24.
    Meloun, M., Bordovská, S., Syrový, T.: A novel computational strategy for the pK(a) estimation of drugs by non-linear regression of multiwavelength spectrophotometric pH-titration data exhibiting small spectral changes. J. Phys. Org. Chem. 20(9), 690–701 (2007)CrossRefGoogle Scholar
  25. 25.
    Maeder, M., King, P.: Analysis of chemical processes, determination of the reaction mechanism and fitting of equilibrium and/or rate constants. https://www.intechopen.com/books/chemometrics-in-practical-applications/analysis-of-chemical-processes-determination-of-the-reaction-mechanism-and-fitting-of-equilibrium-an (2012). Accessed 15 May 2018
  26. 26.
    Zhou, X., Hu, X., Wu, S., Ye, J., Sun, M., Gu, J., Zhu, J., Zhang, Z.: Structures and physicochemical properties of vortioxetine salts. Acta Crystallogr. Sect. B 72(5), 72–73 (2016)CrossRefGoogle Scholar
  27. 27.
    Liao, C.Z., Nicklaus, M.C.: Comparison of nine programs predicting pK(a) values of pharmaceutical substances. J. Chem. Inf. Model. 49(12), 2801–2812 (2009)CrossRefPubMedGoogle Scholar
  28. 28.
    Meloun, M., Bordovská, S.: Benchmarking and validating algorithms that estimate pK(a) values of drugs based on their molecular structures. Anal. Bioanal. Chem. 389(4), 1267–1281 (2007)CrossRefPubMedGoogle Scholar
  29. 29.
    StatSci: S-PLUS 8.2 a new philosophy of data analysis. http://www.insightful.com/products/splus (1994)
  30. 30.
    Balogh, G.T., Gyarmati, B., Nagy, B., Molnar, L., Keseru, G.M.: Comparative evaluation of in silico pK(a) prediction tools on the gold standard dataset. QSAR Comb. Sci. 28(10), 1148–1155 (2009)CrossRefGoogle Scholar
  31. 31.
    Meloun, M., Čapek, J., Mikšík, P., Brereton, R.G.: Critical comparison of methods predicting the number of components in spectroscopic data. Anal. Chim. Acta 423(1), 51–68 (2000)CrossRefGoogle Scholar
  32. 32.
    Meloun, M., Havel, J., Högfeldt, E.: Computation of Solution Equilibria: A guide to Methods in Potentiometry, Extraction, and Spectrophotometry. Ellis Horwood Series in Analytical Chemistry. Ellis Horwood Chichester, England (1988)Google Scholar
  33. 33.
    Meloun, M., Syrový, T., Bordovská, S., Vrána, A.: Reliability and uncertainty in the estimation of pK(a) by least squares nonlinear regression analysis of multiwavelength spectrophotometric pH titration data. Anal. Bioanal. Chem. 387(3), 941–955 (2007)CrossRefPubMedGoogle Scholar
  34. 34.
    Meloun, M., Bordovská, S., Vrána, A.: The thermodynamic dissociation constants of the anticancer drugs camptothecine, 7-ethyl-10-hydroxycamptothecine, 10-hydroxycamptothecine and 7-ethylcamptothecine by the least-squares nonlinear regression of multiwavelength spectrophotometric pH-titration data. Anal. Chim. Acta 584(2), 419–432 (2007)CrossRefPubMedGoogle Scholar
  35. 35.
    Meloun, M., Militký, J., Forina, M.: Chemometrics for Analytical Chemistry. PC-Aided Regression and Related Methods, vol. 2. Ellis Horwood, Chichester (1994)Google Scholar
  36. 36.
    Rigano, C., Grasso, M., Sammartano, S.: Computer-analysis of equilibrium data in solution—a compact least-squares computer-program for acid–base titrations. Ann. Chim. (Rome) 74(7–8), 532–537 (1984)Google Scholar
  37. 37.
    De Stefano, C., Princi, P., Rigano, C., Sammartano, S.: Computer analysis of equilibrium data in solution ESAB2 M: an improved version of the ESAB program. Ann. Chim. (Rome) 77(7–8), 643–675 (1987)Google Scholar
  38. 38.
    Meloun, M., Militký, J., Forina, M.: Chemometrics for Analytical Chemistry. PC-Aided Statistical Data Analysis, vol. 1. Ellis Horwood, Chichester (1992)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Analytical ChemistryUniversity of PardubicePardubiceCzech Republic
  2. 2.Zentiva k.sPragueCzech Republic

Personalised recommendations